In the past, there has been the technical field referred to as “similar image retrieval” for retrieving similar images for one model image presented by a user. NPLT 1 discloses the technique of using color histograms and evenly combining their bins for rough quantification, using those values themselves as features, and measuring a distance of degree of similarity in a feature space to thereby extract similar images. NPLT 2 proposes a system of retrieving similar images from the aspects of color, texture, and shape and defines features similar to those of NPLT 1 for the color, but defines quite different features for the other aspects. NPLT 3 shows a method of similar image retrieval using texture features. Here, an image is transformed into Gabor wavelets, and the set of the mean value of produced high frequency subband values and standard deviation is defined as a feature vector. Then, a technique of extracting an image resembling a texture shown in the Brodatz texture database by a distance comparison in a feature space is disclosed.
On the other hand, unlike similar image retrieval, the technique which can be referred to as “perceptual retrieval” for sorting photos by perceptual adjectives is disclosed in PLT 1. Here, the perception of a photo is described by approximating the photo by three representative colors and comparing this with a database prepared in advance for color designers producing clothing, interiors, and city landscapes and describing relationships between a triadic model and adjective-based verbal impressions. That is, instead of further roughly describing the relationship in the method of NPLT 1 to determine representative colors, a plurality of one to 10 or so pattern models are prepared for one word.
Further, NPLT 4 clarifies the relationship between an image and glossiness. That is, it points out the existence of a deep connection between the asymmetry of a luminance histogram of an image and the mechanism of human perception and judgment of glossiness. Specifically, this clarifies the relationship between the skewness of a luminance histogram and glossiness. In order to form simulation images for a psychological experiment for this purpose, a beta function enabling establishment of correspondence with the skewness is postulated as the model of the histogram, and parameters of that are changed to thereby perform the psychological experiment.